Identification of single nucleotide polymorphisms and haplotypes associated with yield and yield components in soybean (Glycine max) landraces across multiple environments

Abstract

Genome-wide association analysis is a powerful approach to identify the causal genetic polymorphisms underlying complex traits. In this study, we evaluated a population of 191 soybean landraces in five environments to detect molecular markers associated with soybean yield and its components using 1,536 single-nucleotide polymorphisms (SNPs) and 209 haplotypes. The analysis revealed that abundant phenotypic and genetic diversity existed in the studied population. This soybean population could be divided into two subpopulations and no or weak relatedness was detected between pair-wise landraces. The level of intra-chromosomal linkage disequilibrium was about 500 kb. Genome-wide association analysis based on the unified mixed model identified 19 SNPs and 5 haplotypes associated with soybean yield and yield components in three or more environments. Nine markers were found co-associated with two or more traits. Many markers were located in or close to previously reported quantitative trait loci mapped by linkage analysis. The SNPs and haplotypes identified in this study will help to further understand the genetic basis of soybean yield and its components, and may facilitate future high-yield breeding by marker-assisted selection in soybean.

Identification of significant single nucleotide polymorphisms for resistance to maize rough dwarf disease in elite maize (Zea mays L.) inbred lines

“…Exploring the complete genetic architecture to manipulate maize genetically might reduce the losses from this hazardous disease (Ali and Yan 2012). The development of genomics has provided alternative tools to improve breeding efficiency in plant breeding programs, such as molecular markers linked to causal genes and/or QTLs that could be used for marker-assisted selection (MAS) in gene pyramiding (Hao et al 2012b;Xu and Crouch 2008;Collard et al 2005).…”

Selective Genotyping for Marker Assisted Selection Strategies for Soybean Yield Improvement

“…If the environments were similar for adapted maturity, a multi-environment YPM was better for predicting top yielding Plant Genetics, Genomics, and Biotechnology -ISSN 2332-2012 107 lines in multiple individual environments. Bernardo et al (2008) proposed that if the early generation test environments used for MAS are not representative of the environments in which the lines will be grown, then the results seen in early generation testing might not predict the genotypes that are favorable across a broader sample of environments encountered in subsequent replicated trials.…”

“…Yield is a very difficult trait to predict because it can be influenced by many different factors, including genetic and environmental factors and their interactions (Hao et al, 2012;Palomeque et al, 2009 Palomeque et al, , 2012Sebastian et al, 2010). In other words, it is difficult to use QTL selected from one population evaluated in a few environments to another population evaluated in different environments.…”

“…However, one of the seven QTL evaluated by Palomeque et al (2010) was previously reported as being associated with seed yield in diverse genetic backgrounds and environments by other researchers Orf et al 1999;Smalley et al 2004; Specht et al 2001). Hao et al (2012) evaluated a population of 191 soybean landraces in five environments to detect molecular markers associated with soybean yield and its components using 1,536 SNPs. Using genome-wide association, they identified 19 SNPs associated with yield.…”

“…This agrees with the results found in this study where the environment most adaptable to the maturity group made the best predictions. A comparison of previously reported yield QTL that coincide to the yield QTL reported in this study is available in the supplementary material section.Yield is a very difficult trait to predict because it can be influenced by many different factors, including genetic and environmental factors and their interactions (Hao et al, 2012;Palomeque et al, 2009 Palomeque et al, , 2012Sebastian et al, 2010). In other words, it is difficult to use QTL selected from one population evaluated in a few environments to another population evaluated in different environments.…”